A technical guide for architects on moving mainframe applications to cloud, covering the 6 Rs, COBOL refactoring, data integrity, and top migration tools.
For legacy systems and well recognised brands, moving mainframe applications to cloud can be a daunting and intimidating task.
Industry analysts forecast that by the year 2025, an overwhelming 95% of all data workloads will be hosted on cloud-native platforms. And a HUGE reason for that is because it helps make overheads a lot more affordable and more agile improvements later on.
In fact, data indicates an ROI of up to 362% for migrating applications entirely off legacy hardware to distributed environments!
So to help you get an idea, here’s how the step-by-step process for moving mainframe applications to cloud works:
Enterprise architects universally point out application migration as the most difficult, perilous phase of any mainframe modernization initiative. The main reasons moving mainframe applications to cloud turns out so hard include:
The first step in moving mainframe applications to cloud is a strict assessment and discovery phase. Without a completely accurate inventory, teams risk moving over incomplete business logic.
They might also carry over decades of technical debt into their new cloud environments. Legacy systems often contain millions of lines of code. The discovery process cannot fall back on manual code reviews.
Workloads must be broken down based on their business value, technical complexity, risk tolerance, and regulatory compliance requirements. The standard architectural framework for this step of moving mainframe applications to cloud is the 6 Rs strategy. The categories are Rehost, Replatform, Refactor, Replace, Retire, and Retain.
When targeting COBOL, PL/I, or RPG codebases, architects face a technical fork in the road when moving mainframe applications to cloud. Why? Well, Rehosting relies on specialized middleware to serve up native runtime emulation on distributed cloud virtual machines.
Mainframe environments excel at two different computational tasks: concurrent execution of sequential batch workloads, and high-throughput online transaction processing. Decoupling tightly-bound legacy programs calls for specific patterns:
For workloads categorized as Retain, API setup opens up a good way to modernize the mainframe in-place. This plan lets modern cloud-native applications consume legacy business logic. The setup helps pull off moving mainframe applications to cloud without high-risk rewriting.
A flawless application migration falls apart if your data becomes corrupted. Maintaining strict data integrity stands out as the single biggest risk factor during execution.
Mainframe data rarely resides in simple relational tables. The information usually ends up stuck in hierarchical databases or VSAM datasets.
The vendor ecosystem puts out a range of highly specialized tools. These are tuned up for different migration methods.
AWS Transform serves as an intelligent, purpose-built engine. The platform carries out deep codebase analysis. The system automatically refactors monolithic COBOL workloads into cloud-optimized Java Spring Boot applications.
Most importantly, the tool guarantees bit-identical outputs to the original COBOL. This opens up flawless regression testing for enterprise engineering teams moving mainframe applications to cloud.
IBM Code Generation for Z works with generative AI models. These are specifically fine-tuned on mainframe patterns and legacy languages like COBOL and PL/I.
The assistant incrementally translates code to Java. The service hooks up directly with IBM ADDI. This wards off the AI hallucinations common in generic models when moving mainframe applications to cloud.
Astadia relies on a proprietary Rules-Based Transformation Engine to carry out mass source code changes.
This sets up an end-to-end framework from static discovery to final execution on Azure, AWS, or Google Cloud for moving mainframe applications to cloud.
This OpenText Enterprise Server serves up native COBOL, PL/I, and JCL runtime emulation on distributed servers like AWS EC2 or Azure.
The software asks for absolute zero source code modification. The system steps in as a highly validated intermediate derisking step. The emulator is highly regarded for regulated environments where strict functional equivalence is legally required.
OpenFrame by TmaxSoft draws on advanced compiler technology to run UI, logic, and data natively in open environments.
The platform swaps out CICS, JCL, and IMS for standards-based equivalents. The product props up a massive enterprise-grade scale of over 100K MIPS.
OpenLegacy gets around traditional middleware by plugging directly into the legacy stack.
The hub leans on machine learning to map out dependencies. The tool auto-generates REST/Kafka APIs. This significantly knocks down network latency and shores up safe, phased coexistence when moving mainframe applications to cloud.
At Entrans, we map out mainframe migration as a precise and technical process - turning the move into less of a risky change.
This takes on the most complex COBOL and PL/I environments without throwing off primary business rules when moving mainframe applications to cloud.
Having partnered up with Fortune200 clients, banks, and healthcare providers, the Entrans team also understands the weight that these migrations carry.
Want to get a roadmap for moving your mainframe application to the cloud? Book a free consultation call!
Rehosting lifts and shifts the mainframe application exactly as-is into a cloud-based runtime emulator. This calls for no changes to the legacy source code. Replatforming pushes ahead a step further by updating the underlying hardware layer. An example means moving execution over into managed Linux containers. This keeps primary business logic intact.
Timelines can stretch out from a few weeks for targeted API setup to several years for full, enterprise-wide refactoring. Budgets range from a few thousand dollars for pilots to well over $100 million for multinational systems. These are systems attempting to break away from the mainframe entirely.
Data integrity starts with pre-migration data profiling.This helps to clean historical inconsistencies. During migration, teams tap into Change Data Capture (CDC) for continuous replication. These teams typically run systems in parallel to prove calculated outputs turn out mathematically identical.
Modern IBM Z mainframes continue to churn out unparalleled stability, physical security, and sub-millisecond I/O processing for high-volume transactions. Primary systems are often held onto simply to ward off the massive risk of downtime. This leads into hybrid phased coexistence plans.
-1.svg){kind=small}
